An Efficient Character Recognition Technique Using K-Nearest Neighbor Classifier

  • Authors

    • Nawaf Hazim Barnouti
    • Mohammed Abomaali
    • Mohanad Hazim Nsaif Al-Mayyahi
    https://doi.org/10.14419/ijet.v7i4.21555

  • Abstract

    Optical Character Recognition (OCR) Systems offers human machine interaction and are commonly used in several important applications. A lot of research has already been accomplished on the character recognition in different languages. This paper presents a technique for recognition of Printed text with noise using Optical Character Recognition (OCR). The main steps of this system are pre-processing of the text including converting the text image to black/white and remove the noise from the text image, segmentation of the text image to each character, Feature extraction using zoning-based technique and classification. The System is implemented using MATLAB 2016a software application program and is still under development. Noise is removed from all the text images. The quality of the input document is very important to achieve high accuracy. The system is able to recognize characters in different 50 images.

  • References

    1. [1] N. Venkata Rao, A.S.C.S.Sastry, A.S.N.Chakravarthy, and K. P, “Optical Character Recognition Technique Algorithms,†Journal of Theoretical and Applied Information Technology, vol. 83, no. 2, pp. 275-282, 2016.

      [2] A. H. Ahmed, M. Afifi, M. Korashy, E. K.William, M. A. El-sattar, and Z. Hafez, “OCR System for Poor Quality Images Using Chain-Code Representation,†The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015). Beni Suef, Egypt. Springer, Cham, pp. 151-161, 2016.

      [3] C. Patel, A. Patel, and D. Patel, “Optical Character Recognition by Open Source OCR Tool Tesseract: A Case Study,†International Journal of Computer Applications, vol. 55, no. 10, pp. 50-56, 2012.

      [4] A. M. A. M. Asif, S. A. Hannan, Y. Perwej, and M. A. Vithalrao, “An Overview And Applications Of Optical Character Recognition,†International Journal of Advance Research In Science And Engineering, vol. 3, no. 7, pp. 261-274, 2014.

      [5] AnandMishra, K. Alahari, and C. V. Jawahar, “Top-Down and Bottom-up Cues for Scene Text Recognition,†Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 2687-2694, 2012.

      [6] L. Neumann, and J. Matas, “A method for text localization and recognition in real-world images,†Asian Conference on Computer Vision. Springer, Berlin, Heidelberg, pp. 770-783, 2010.

      [7] A. Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang, D. J. Wu, and A. Y. Ng, “Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning,†International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 440-445, 2011.

      [8] S. Babu, Z. A. Masood, S. Munir, S. Adnan, and I. Bari, “Android Based Optical Character Recognition for Noisy Document Images,†International Journal of Computer Science and Information Security (IJCSIS), vol. 14, no. 1, pp. 34-37, 2016.

      [9] T. E. d. Campos, B. R. Babu, and M. Varma, “Character Recognition In Natural Images,†2009.

      [10] L. Neumann, and J. Matas, “Real-Time Scene Text Localization and Recognition,†Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 3538-3545, 2012.

      [11] M. A. Mohamad, D. Nasien, H. Hassan, and H. Haron, “A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition,†International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, no. 2, pp. 204-212, 2015.

      [12] A. Fabijańska, and D. Sankowski, “Image Noise Removal – The New Approach,†9th International Conference on the Experience of Designing and Applications of CAD Systems in Microelectronics (CADSM'07). IEEE, pp. 457-459, 2007.

      [13] S. Kaur, “Noise Types and Various Removal Techniques,†International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), vol. 4, no. 2, pp. 226-230, 2015.

      [14] K. C. Nguyen, and N. Masaki, “Text-Line and Character Segmentation for Off-line Recognition of Handwritten Japanese Text,†IEICE technical report 115.517 pp. 53-58, 2016.

      [15] M. Sarfraz, S. N. Nawaz, and A. Al-Khuraidly, “Offline Arabic Text Recognition system,†Proceedings International Conference on Geometric Modeling and Graphics. IEEE, pp. 30-35, 2003.

      [16] P. Singh, and S. Budhiraja, “Feature Extraction and Classification Techniques in O.C.R. Systems for Handwritten Gurmukhi Script – A Survey,†International Journal of Engineering Research and Applications (IJERA), vol. 1, no. 4, pp. 1736-1739, 2011.

      [17] M. Z. Hossain, M. A. Amin, and H. Yan, “Rapid Feature Extraction for Optical Character Recognition,†arXiv preprint arXiv:1206.0238 2012.

      [18] P. Vithlani, and C.K.Kumbharana, “Structural and Statistical Feature Extraction Methods for Character and Digit Recognition,†International Journal of Computer Applications, vol. 120, no. 24, pp. 43-47, 2015.

      [19] Y. Elglaly, and F. Quek, “Isolated Handwritten Arabic Characters Recognition using Multilayer Perceptrons and K Nearest Neighbor Classifiers,†pp. 1-6, 2011.

      [20] M. Rajalingam, P. Sumari, and V. Raman, “Text Detection and Extraction from Document Images using K-Nearest Neighbor Rule,†International Journal of Computer and Information Technology, vol. 3, no. 4, pp. 731-736, 2014.

      [21] YingquanWu, K. Ianakiev, and V. Govindaraju, “Improved k-nearest neighbor classication,†Pattern Recognition, vol. 35, no. 10, pp. 2311-2318, 2002.

      [22] D. K. Patel, T. Som, S. K. Yadav, and M. K. Singh, “Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric,†Journal of Signal and Information Processing, vol. 3, no. 2, pp. 208-214, 2012.

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  • How to Cite

    Barnouti, N. H., Abomaali, M., & Al-Mayyahi, M. H. N. (2018). An Efficient Character Recognition Technique Using K-Nearest Neighbor Classifier. International Journal of Engineering & Technology, 7(4), 3148-3153. https://doi.org/10.14419/ijet.v7i4.21555

    Received date: 2018-11-25

    Accepted date: 2018-11-25